We found 31 results that contain "tag 1"

Edited -- Divide the total number of users by the desired duration to determine the ramp-up rate. For example:

If you want to ramp up 100 users evenly over 1 minute:
Ramp-Up Period = Total number of users / Desired duration
= 100 users / 1 minute
= 100 users/minute
In this case, each user would start every 0.01 minutes (or every 0.6 seconds).

If you want to ramp up 100 users evenly over 5 minutes:
Ramp-Up Period = Total number of users / Desired duration
= 100 users / 5 minutes
= 20 users/minute
In this case, each user would start every 0.05 minutes (or every 3 seconds).

Consider Realistic Scenarios:
While evenly distributing users is a common approach, consider if it reflects the real-world usage pattern of your application. Sometimes, you might want to simulate a more gradual or sudden increase in load to mimic how users interact with the system.

Test Iteratively and Adjust as Needed:
It's essential to iterate on your load tests, adjusting parameters like the ramp-up period based on initial results. This iterative process helps refine the test plan to better simulate real-world scenarios and uncover performance bottlenecks.

Edited -- A natural disaster is the highly harmful impact on a society or community following a natural hazard event. The term "disaster" itself is defined as follows: "Disasters are serious disruptions to the functioning of a community that exceed its capacity to cope using its own resources. Disasters can be caused by natural, man-made and technological hazards, as well as various factors that influence the exposure and vulnerability of a community."[17]

The US Federal Emergency Management Agency (FEMA) explains the relationship between natural disasters and natural hazards as follows: "Natural hazards and natural disasters are related but are not the same. A natural hazard is the threat of an event that will likely have a negative impact. A natural disaster is the negative impact following an actual occurrence of natural hazard in the event that it significantly harms a community.[1] An example of the distinction between a natural hazard and a disaster is that an earthquake is the hazard which caused the 1906 San Francisco earthquake disaster.

A natural hazard[18] is a natural phenomenon that might have a negative effect on humans and other animals, or the environment. Natural hazard events can be classified into two broad categories: geophysical and biological.[19] Natural hazards can be provoked or affected by anthropogenic processes, e.g. land-use change, drainage and construction.[20]

There are 18 natural hazards included in the National Risk Index of FEMA: avalanche, coastal flooding, cold wave, drought, earthquake, hail, heat wave, tropical cyclone, ice storm, landslide, lightning, riverine flooding, strong wind, tornado, tsunami, volcanic activity, wildfire, winter weather.[1] In addition there are also tornados and dust storms.

Peer tutoring is most effective when training is provided to participating students (Piffner, 2011). Tutors need to be taught how to be prepared with materials needed for the session and how
to give positive and corrective feedback to their partner (Greenwood & Delquadri,
1995).

The IoT-Based Smart Farming Cycle
The core of IoT is the data you can draw from things and transmit over the internet. To optimize the farming process, IoT devices installed on a farm should collect and process data in a repetitive cycle that enables farmers to react quickly to emerging issues and changes in ambient conditions. Smart farming follows a cycle similar to this one:

1. Observation . Sensors record observational data from the crops, livestock, soil, or atmosphere.

2. Diagnostics. The sensor values are fed to a cloud-hosted IoT platform with predefined decision rules and models—also called "business logic"—that ascertain the condition of the examined object and identify any deficiencies or needs.

3. Decisions . The user and/or the machine learning-driven components of the IoT platform assess the revealed issues to decide if location-specific treatment is necessary.

4. Action . After end-user evaluation and action, the cycle repeats from the beginning.

The IoT-Based Smart Farming Cycle
The core of IoT is the data you can draw from things and transmit over the internet. To optimize the farming process, IoT devices installed on a farm should collect and process data in a repetitive cycle that enables farmers to react quickly to emerging issues and changes in ambient conditions. Smart farming follows a cycle similar to this one:

1. Observation . Sensors record observational data from the crops, livestock, soil, or atmosphere.

2. Diagnostics. The sensor values are fed to a cloud-hosted IoT platform with predefined decision rules and models—also called "business logic"—that ascertain the condition of the examined object and identify any deficiencies or needs.

3. Decisions . The user and/or the machine learning-driven components of the IoT platform assess the revealed issues to decide if location-specific treatment is necessary.

4. Action . After end-user evaluation and action, the cycle repeats from the beginning.

Child group post by admin:
API testing is a procedure developers use to evaluate APIs' functionality, efficacy, and security. Before releasing their software, the results of API testing will inform developers if problem fixes and patches are required. They accomplish this through a simulation that entails sending queries that would reach the API when it is accessible to its users, regardless of whether it is authentic. They observe the API to determine how it will react to this volume of queries. If the results are positive, integrating the API is secure. If not, they will be required to fix it.

Parent group post by admin:
Direct interaction:
API tests send requests directly to API endpoints and analyze the responses to ensure they meet expected outcomes. This involves checking status codes, response times, and the structure and content of the data returned.
Focus on business logic:
API testing primarily validates the underlying business rules and data flow within an application, independent of the user interface.
Early defect detection:
By testing APIs early in the development lifecycle, issues can be identified and resolved before they become more complex and costly to fix in later stages.
Automation potential:
API tests are highly automatable, allowing for frequent and efficient execution, which is crucial for continuous integration and continuous delivery (CI/CD) pipelines.
Types of API tests:
This can include functional testing (verifying core functionality), performance testing (measuring response times under load), security testing (identifying vulnerabilities), and integration testing (ensuring seamless communication between APIs and external services).